Machine-to-machine technology is undergoing a transformation from proprietary, vertical-market segment solutions to horizontal platforms with applications in multiple usage areas, for example market segments. A much broader collection of data which can be subjected to data-mining for adding more value than current vertical-integrated solutions is enabled. However, a handling of this so-called “BigData”, which is in the order of Petabytes, Exabytes or an even higher order is difficult. For example, a fast integration, filtering and abstraction for multiple application areas ranging from e-health to agriculture or energy-saving to distributed-manufacturing is one of the problems.
One of the conventional machine-to-machine technologies is so-called oneM2M standardizing a common M2M service platform for reducing fragmentation and enabling faster integration and growth of applications. Similarly BigData platforms respectively BigData repositories are also fragmented and have also to be evolved to a common BigData platform, such as that depicted in FIG. 1.
Many machine-to-machine service providers and operators intend to operate machine-to-machine platforms as well as BigData platforms respectively BigData repositories. Therefore, integration of BigData platforms and machine-to-machine platforms is desired to enable mashed-up applications.